Two linear subpattern dimensionality reduction algorithms

BEN Xian-ye¹, MENG Wei-xiao², WANG Ze³, WANG Ke-jun⁴

1.School of Information Science and Engineering, Shandong University, Jinan 250100, China;2.School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China;3.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China;4.College of Automation, Harbin Engineering University, Harbin 150001, China



Abstract:

This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.ª¤

Key words:  subpattern dimensionality reduction  Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA)  Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA)  gait recognitionª¤

DOI£º10.11916/j.issn.1005-9113.2012.05.008

Clc Number:TP391.41

Fund: